Such a calibration pattern can for example be used for calibrating the orientation of a camera mounted to a vehicle. The image of the calibration pattern is then acquired by means of this camera. Especially for such a calibration, but also for different possible purposes in different contexts, it can be necessary to identify characteristic points of the calibration pattern within the image of the calibration pattern. Such characteristic points, however, might not be identifiable by a single procedure or a single criterion. Therefore, it might be useful to first identify candidate points having some necessary characteristics of the characteristic points to be identified, with these characteristics, however, not being sufficient for reliable identification of the “true” characteristic points of the calibration pattern. As a result, the candidate points ideally comprise all characteristic points to be identified, though they might also comprise other points only resembling characteristic points to a certain extent.
The method for identification of such candidate points as possible characteristic points of the calibration pattern does not necessarily have to be executed on the image as directly obtained photographically from the calibration pattern. Instead the image might be preprocessed, for example, in a manner such that it is better adapted to be searched for candidate points.
In particular, candidate points can be characterized by an at least local maximum or minimum of pixel values of pixels of the image. Preferably, the resolution of the image is such that a possible characteristic point of the calibration image is represented in the image by an area comprising several pixels. The pixel values of this area might then form an elevation or a depression compared to the pixel values of surrounding pixels. To find such an elevation or depression, the pixel value of each of the pixels of the image could be compared to a threshold, with the location of a candidate point being, for example, determined as an apex of a found elevation or depression. However, due to noise or other errors in the image, identifying as candidate points all pixels having a locally maximal or minimal pixel value and/or being the apex of a pixel value elevation or depression could lead to too many false positives.